Large-scale retrieval is to recall relevant documents from a huge collection
given a query. It relies on representation learning to embed documents and
queries into a common semantic encoding space. According to the encoding space,
recent retrieval methods based on pre-trained language models (PLM) can be
coarsely categorized into either dense-vector or lexicon-based paradigms. These
two paradigms unveil the PLMs' representation capability in different
granularities, i.e., global sequence-level compression and local word-level
contexts, respectively. Inspired by their complementary global-local
contextualization and distinct representing views, we propose a new learning
framework, UnifieR, which unifies dense-vector and lexicon-based retrieval in
one model with a dual-representing capability. Experiments on passage retrieval
benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme
is further presented with even better retrieval quality. We lastly evaluate the
model on BEIR benchmark to verify its transferability.

本论文提出了一种名为 UnifieR 的学习框架，它将基于预训练语言模型的密集向量和基于词汇表的检索统一在一个模型中，并在多个基准测试中验证了其有效性和可转移性。